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Research Article
Vol. 35, Issue 1
Enhancing Volatility Forecasting: A Comparative Study of GARCH(1,1) Models with Non-Normal Error Distributions
This study proposes a GARCH(1, 1) model with generalized logistic distribution (GLD) errors to better capture skewness and kurtosis in financial returns. Using crude oil price data, it compares GLD with GED and Student’s t-distributions. Results show the GLD-based model outperforms others in forecasting accuracy and volatility modeling.
